Smart Waste Sorting, Recycling Optimization, and Circular Economy
Module: Sustainable Tech | Difficulty: Premium
Waste Composition Model
Recycling Efficiency
Comparison
| Stream | Typical % | Recycling Rate | Market Value ($/ton) | |
|---|---|---|---|
| Organic | 30-40% | 10-20% | -5 to 0 |
| Plastic | 10-15% | 30-50% | 50-200 |
| Paper | 20-25% | 60-70% | 20-80 |
| Metal | 5-10% | 70-90% | 100-500 |
| Glass | 5-10% | 30-40% | 10-30 |
Python Implementation
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
class WasteSorter:
def __init__(self, n_classes=10):
base = tf.keras.applications.ResNet50(weights='imagenet', include_top=False)
self.model = tf.keras.Sequential([
base, layers.GlobalAveragePooling2D(),
layers.Dense(256, activation='relu'), layers.Dropout(0.5),
layers.Dense(n_classes, activation='softmax')
])
def predict_waste_type(self, image):
img = tf.expand_dims(tf.keras.preprocessing.image.img_to_array(image), 0)
return np.argmax(self.model.predict(img)[0])
Research Insight: Computer vision systems for waste sorting now achieve 95-98% accuracy for common waste streams.